Approach to Aerodynamic Design Through Numerical Optimization
Bibliographic record
Abstract
A multipoint optimization approach is used to solve aerodynamic design problems encompassing a broad range of operating conditions in the objective function and constraints. The designer must specify the range of on-design operating conditions, the objective function to be minimized, a weighting function based on the mission or fleet requirements, and a set of performance and geometric constraints. Based on this designer input, a weighted-integral objective function is developed. The numerical optimization problem is then formulated as a constrained multipoint problem with the weight assigned to each operating condition determined by a quadrature rule. The approach is illustrated with several design problems for transonic civil transport aircraft and is extended to the formulation of aircraft range and endurance objective functions for use in the design of an unmanned aerial vehicle. The results demonstrate that the approach enables the designer to design an airfoil that is precisely tailored to the problem specification. Pareto fronts are presented as a means of providing the designer with information on tradeoffs that can be used to guide the problem specification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".